https://orcid.org/0000-0002-5028-9901
Abstract
Responsible data sharing in clinical research can enhance the transparency and reproducibility of research evidence, thereby increasing the overall value of research. Since 2024, more than 5,000 journals have adhered to the International Committee of Medical Journal Editors (ICMJE) Data Sharing Statement (DSS) to promote data sharing. However, due to the significant effort required for data sharing and the scarcity of academic rewards, data availability in clinical research remains suboptimal. This study aims to explore the impact of biomedical journal policies and available supporting information on the implementation of data availability in clinical research publications This cross-sectional study will select 303 journals and their latest publications as samples from the biomedical journals listed in the Web of Science Journal Citation Reports based on stratified random sampling according to the 2023 Journal Impact Factor (JIF). Two researchers will independently extract journal data-sharing policies from the submission guidelines of eligible journals and data-sharing details from publications using a pre-designed form from Apr 2025 to Dec 2025. The data sharing levels of publications will be based on the openness of the data-sharing mechanism. Binomial logistic regression analyses will be used to identify potential journal factors that affect publication data-sharing levels. This protocol has been registered in Open Science Framework (OSF) Registries: https://doi.org/10.17605/OSF.IO/EX6DV.
Citation: Zhao P, Zhang X, Dai L, Ma B, Duan Y, Xu Y, et al. (2025) Journal data-sharing policies and its impact in publications: A cross-sectional study protocol. PLoS One 20(9): e0331697. https://doi.org/10.1371/journal.pone.0331697
Editor: Jafar Kolahi, Dental Hypothesis, IRAN, ISLAMIC REPUBLIC OF
Received: April 3, 2025; Accepted: August 19, 2025; Published: September 2, 2025
Copyright: © 2025 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets used and/or analyzed during the current study have been provided in the appendixes. The lead authors (the manuscript’s guarantors) affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned (and, if relevant, registered) have been explained.
Funding: This work was supported by the Youth Fund of the National Natural Science Foundation of China (82004226), Guangzhou Health Science and Technology Project (2023A031002), the Guangzhou Key Research and Development Program (2023B03J1302), and the Guangzhou Municipal Key Discipline in Medicine (2025-2027). None of the sponsors had a role in any aspect of this study, including the design and implementation of the study; collection, management, analysis, and interpretation of the data; and preparation, review, approval of the manuscript, or decision to publish.
Competing interests: All authors have nothing to declare.
Introduction
Responsible sharing of data from clinical trials can increase the transparency of study evidence, provide opportunities for researchers to strengthen their study designs, validate study findings, and generate new key findings through the synthesis of data sharing, which maximizes scientific knowledge for researchers and participants in clinical trials [1]. However, data sharing in clinical research publications is unsatisfactory, with a prevalence of actual data sharing of only 2% between 2016 and 2021 [2]. The absence and opacity of large amounts of clinical trial data may be detrimental to developing more efficient and effective public health service [3]. Therefore, an increasing number of biomedical scientists and stakeholders advocated for data sharing policy [4–6].
However, promoting data sharing is challenging and fraught with technical, motivational, economic, political, legal, and ethical barriers [3]. Specifically, while a standard de-identification process can reduce the risk of patient information being re-identified [7], the lack of regulation of shared data use can lead to concerns about inappropriate reuse and re-identification [8,9]. Secondly, due to the chronic lack of resources in clinical practice (e.g., time, staff, funding), researchers prioritize publishing study results over sharing data [10], as academic publications rather than data ownership represent scientific productivity and capability for authors [11]. Finally, the author’s concerns about losing the advantages of secondary publication, unwarranted questions about the authenticity and integrity of shared data [12], and the lack of a simple, uniform, and efficient data-sharing process hinder data sharing in clinical research [13].
Current measures to promote data sharing include emphasizing data authorship [14], funding data-sharing platforms (e.g., data repositories, online sharing platforms) [15], and journal data-sharing requirements [16]. However, the contribution of data authors is insufficiently recognized, and data authorship is rarely rewarded with academic recognition and rewards [17,18]. Funding for data repositories does not directly incentivize researchers to share data. Under publication pressure, journal data-sharing requirements seem to be a coin step in promoting data sharing [19].
In 2017, the International Committee of Medical Journal Editors (ICMJE) required that publications should include a specific Data Sharing Statement (DSS) to promote new norms of data sharing [20]. More than 5,000 journals followed the ICMJE recommendations [16], but around 56% of journals did not state a specific data-sharing policy, possibly due to concerns about increased costs and loss of submissions [21]. However, studies have found that the strength of journal requirements of policies (e.g., encourage, mandatory) [22] and the availability of resources (e.g., free data repository links, data-sharing handbook) may influence publications adherence to journal policies [23]. To date, it lacks comprehensive surveys on the impact of journal policies on publication data-sharing implementations.
Hence, we undertake this research to extract journal policy data and available supporting information from submission guidelines and assess the data-sharing implementations from the journal’s publication to explore the potential impact of journal data-sharing policies on the publication data-sharing level. We hope this research will offer a deeper insight into the data-sharing policies and promote the further development of the data-sharing new normal.
Methods
This is an analytical, cross-sectional, descriptive study. Data collection will be carried out in biomedical journals (from Web of Science Journal Citation Reports [24]) by two experienced researchers. We will gather detailed data-sharing policies and data-sharing statements from journal submission guidelines and its latest publications. This protocol is registered and publicly available via the Open Science Framework [25] (Registration DOI: https://doi.org/10.17605/OSF.IO/EX6DV). This study will follow STORBE guidelines for cross-sectional study [26].
Sample size calculation
The minimum sample size (N) for this study was estimated by Events Per Variable (EPV) formula [27] and the formula
(for binary outcome) [28]. This was conducted to ensure that sufficient power will be available to obtain journal adherence levels for data sharing. When hypothesize EPV = 10, our study has one independent variable and 8 co-variables (K), and the proportion (p) of journals that did not required data-sharing statements is 0.27 [16], we generate the sample size is 297. When we set 0.05 for a margin error (E), the proportion (p) is 0.27 [16], we get the hypotheses result in sample sizes is 303. Therefore, we will use the largest sample size of 303 in this study.
Data source and sampling methods
Since the Web of Science Journal Citation Reports only supports exporting 600 records at a time, we exported journals individually according to their category (biomedical journals), then excluded duplicate journals (a journal may have multiple category labels) by International Standard Serial Number (ISSN)/ eISSN to finalize the identification of 6313 journals We sorted and coded the journals according to Journal Impact Factors (2023 JIF). Details of the coding are in S1 Appendix.
We will fulfill stratified sampling based on the 2023 JIF, and the block group lengths will be set to NA- < 1.0, 1.0- < 3.0, 3.0- < 5.0, 5.0- < 10.0, 10.0- < 20.0, ≥ 20.0. If the extracted journals do not meet the inclusion criteria, we will use the pre-specified replacement hierarchy. Specifically, the first principle is to use journals with the same JIF as the ineligible journals instead, and the second principle is to use the code closest to the ineligible journal when multiple journals with the same JIF exist (prioritizing the next code, or the previous code if the JIFs are different). Any details of the replacement will be published along with the publication.
Eligibility criteria
Journal inclusion criteria: (1) biomedical journal; (2) published at least one clinical research in the latest issue in 2024; (3) English journals; (4) peer-reviewed journals. Journal exclusion criteria: (1) publish only conference papers; (2) without submission guidelines, (3) not publish clinical studies.
Studies will be the latest clinical research publications in the latest issue of eligible journals. The specific order of clinical research [29] and the Oxford Centre for Evidence-based Medicine levels [30] (see S2 Appendix), which will be: (1) randomized controlled trials, (2) non-randomized studies (quasi-experiment, field trial, community trial), (3) cohort studies, (4) case-control studies, (5) cross-sectional studies, (6) case reports, (7) case series, (8) surveillance studies, and (9) qualitative research. The exclusion criteria will be (1) full text unavailable; (2) non-human studies; (3) non-English publications.
Two researchers (XZ and BM) will independently check journal homepages and their latest issues to find eligible journals and studies. Then, we will record the journal submission guidelines link and download the study’s full text. Any discrepancies will be discussed and resolved with the senior researcher (PZ).
Study status and timeline
Data will be collected from Apr 2025 through Dec 2025 (the detailed data extraction time for each journal will be presented in future studies). Two researchers (XZ and BM) will extract data independently in a standardized data extraction form in Microsoft Excel 2019 (Microsoft Excel 2019 MSO 2210 Build 16.0.15726.20188 32). Any discrepancy in the data extraction will be resolved via discussion or adjudication by the senior reviewer (PZ). Before the formal data collection, we will perform at least three rounds of pilot testing on at least 10 journals and 10 studies to ensure consistency and accuracy, and only the kappa coefficients > 0.90 will start the formal extraction.
Data extraction
The following information of journals will be extracted: (1) journal name, (2) JCR abbreviation, (3) publisher, (4) ISSN, (5) eISSN, (6) category, (7) 2023 JIF, (8) JIF quartile, (9) percentage of citable Open Assess (OA), (10) whether the submission guidelines contain a data availability statements/ data-sharing statements, (11) follows what kind of data policy (e.g., Springer Nature research data policy), (12) requirements level of data-sharing (e.g., encourage, should, required, must), (13) any indication of what should be included in data sharing, (14) guidelines for sharing data, (15) are data repositories recommended, (16) guidelines/ links for data repositories, and (17) whether easy to obtain data sharing policies.
Data extraction in studies: (1) volume of journal, (2) publish date, (3) name, (4) first author, (5) research type, (6) registry, (7) funding, (8) interest, (9)whether it contains a data sharing statement. Then we will used the ICMJE DSS [20] to collect possible structures of data availability of the study, which contains: (1) will individual participant data be available, (2) what data in particular will be shared, (3) what other documents will be available, (4) when will data be available, (5) with whom, (6) for what types of analyses, (7) by what mechanism will data be available. Finally, we will grade the data sharing levels according to the openness of the data-sharing mechanism. We will define data-sharing with raw data provided in open-access repositories/ appendix as “high level of data-sharing”, data can be available by contacting authors with reasonable requests or data will be available at additional time (e.g., one year after publication), no mention of data-sharing mechanism as “small level of data-sharing”, and without data-sharing as “without data-sharing”.
Data clean and confounder identification
We will classify the requirements level of data-sharing policies as “high” (mandatory data sharing), “small” (encouraging data sharing), and “no” (no mention of data sharing) to explore the potential impact of policies on data-sharing level. We identified all variables that may be involved in the causal effects of journal data sharing policies and data sharing practices by discussing with methodological experts with prior knowledge, and presented them in a directed acyclic graphs (DAGitty v3.1) [31] (see S3 Appendix). The following co-variables will be considered for inclusion in the final model: journal JIF, percentage of citable OA, whether easy to obtain data sharing policies, data sharing supporting information (requirements level of data-sharing, indication of data-sharing, guidelines for sharing data, data repositories recommendation, and guidelines/ links for data repositories).
Data analysis
The analyses and stratified sampling will be conducted in R software (Version R 4.4.3; R Core Team, 2025; R Foundation for Statistical Computing, Vienna, Austria). The primary outcome is to explore the journal data-sharing policies, available supporting information, and data-sharing level in publications. Then we will further explore the potential impact of data-sharing policy on the data-sharing level. Statistical significance will be defended with two-sided 95% Confidence Intervals (CIs), P < 0.05.
Descriptive statistics will be presented in counts (n) and percentages (%) by charts. We will describe the journal data-sharing policies and supporting information at the journal level. Describe the specific data-sharing details at the publication level.
Binomial logistic regression analyses will be used to estimate the adjusted odds ratios (aORs) and 95% confidence intervals (CIs) of the data-sharing policy with data-sharing level. We will use data-sharing policy as the dependent variable, use data-sharing level as the independent variable, and journal JIF, percentage of citable OA, whether easy to obtain data sharing policies, data sharing supporting information (requirements level of data-sharing, indication of data-sharing, guidelines for sharing data, data repositories recommendation, and guidelines/ links for data repositories) as the co-variables.
Discussion
Good data sharing and management should meet Findability, Accessibility, Interoperability, and Reusability (FAIR) principles to maximize the data value from clinical trials [32]. However, responsible data sharing requires significant efforts in data collection, archival, sharing, and long-term maintenance, but the incentives for data sharing are scarce [14]. Those disproportionate data-sharing efforts and incentives significantly affect data sharing in clinical research and may persist in modern scientific evaluation systems [17]. Therefore, it is critical to provide extra incentives and assistance for authors to promote data sharing. Our study will explore potential factors at the journal level.
It is known that journal data-sharing policies are requirements rather than incentives for authors. Current journal data-sharing policies typically require a declaration of data-sharing/ data availability statements without any defining details about data accessibility [33]. Under publication pressure, 52%−96% of biological publications contained a data-sharing statement, but 76.6% − 93% of studies did not share data after published studies [34,35]. Declarations of various reasons for data unavailability in the DSS, not responding or declining to the requests for the data, and responding to data assessment requests but failing to provide data are the most common types of data unavailability [34–36]. Therefore, developing more detailed data-sharing policies and providing additional support for data-sharing, such as guidelines for the use of open data repositories, professional de-identification process training, and technical support for data-sharing, may be able to facilitate data-sharing.
Strengths and limitations of the study
The strengths of this study are: (1) There will be no limitations of biomedical journal categories and disease types of the included samples, allowing for a more comprehensive reflection of the data sharing policies and actual data-sharing practices in clinical research. (2) We systematically explored details related to the strength of data sharing requirements and accompanying supporting information for journals, which may be beneficial to the development of journal data sharing policies. (3) This will be the first study to explore the journal data-sharing policies and actual data-sharing practices in their clinical research publications, thereby establishing a solid foundation for data sharing as the new normal in clinical research in the future.
The limitations of this study are: (1) The lag in clinical research publications. While we will include the most recent publications from journals, some journals may have received their most recent publications in a period before the policy update (especially for journals with a high volume of submissions but fewer publications). Those publications may not provide timely feedback on the journal’s policy requirements, depending on whether or not the journal will review incoming manuscripts again for formatting before publication. (2) We will only include journals with official websites in English; study findings may not be generalizable to journals published in other languages. (3) We may be unable to avoid subjective bias in policy interpretation, even if we standardize inter-assessor agreement between reviewers. Analysis of study outcomes should be interpreted with caution.
Conclusion
This study serves as the inaugural examination of the application of journal data-sharing policies and available supporting information to publications. The insights drawn from this study may have immense potential to promote the further development of the data-sharing new normal.
Supporting information
S2 Appendix. The specific order of included studies.
https://doi.org/10.1371/journal.pone.0331697.s002
(DOCX)
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